Lineage plasticity in prostate cancer depends on JAK/STAT inflammatory signaling

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Abstract

Drug resistance in cancer is often linked to changes in tumor cell state or lineage, but the molecular mechanisms driving this plasticity remain unclear. Using murine organoid and genetically engineered mouse models, we investigated the causes of lineage plasticity in prostate cancer and its relationship to antiandrogen resistance. We found that plasticity initiates in an epithelial population defined by mixed luminal-basal phenotype and that it depends on elevated JAK and FGFR activity. Organoid cultures from patients with castration-resistant disease harboring mixed-lineage cells reproduce the dependency observed in mice, by upregulating luminal gene expression upon JAK and FGFR inhibitor treatment. Single-cell analysis confirms the presence of mixed lineage cells with elevated JAK/STAT and FGFR signaling in a subset of patients with metastatic disease, with implications for stratifying patients for clinical trials.

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limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Author and article information

Contributors

Joseph M. Chan: (View ORCID Profile)

Jimmy L. Zhao: (View ORCID Profile)

Manu Setty: (View ORCID Profile)

Jungmin Choi: (View ORCID Profile)

Kayla E. Lawrence: (View ORCID Profile)

Tianhao Xu: (View ORCID Profile)

Ignas Masilionis: (View ORCID Profile)

Shangqian Wang: (View ORCID Profile)

Michael J. Morris: (View ORCID Profile)

Linas Mazutis: (View ORCID Profile)

Ronan Chaligne: (View ORCID Profile)

Yu Chen: (View ORCID Profile)

David W. Goodrich: (View ORCID Profile)

Dana Pe’er: (View ORCID Profile)

Charles L. Sawyers: (View ORCID Profile)

Journal

Title: Science

Abbreviated Title: Science

Publisher: American Association for the Advancement of Science (AAAS)

ISSN (Print): 0036-8075

ISSN (Electronic): 1095-9203

Publication date (Electronic): August 18 2022

Affiliations

[1 ]Department of Medicine, Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

[2 ]Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

[3 ]Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

[4 ]Department of Genitourinary Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

[5 ]Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA.

[6 ]Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

[7 ]Department of Computer Science, Columbia University, New York, NY 10027, USA.

[8 ]Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Korea.

[9 ]Molecular Cytology Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

[10 ]Institute of Biotechnology, Life Sciences Centre, Vilnius University, Vilnius, Lithuania.

[11 ]Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

Article

DOI: 10.1126/science.abn0478

PubMed ID: 35981096

SO-VID: ae034501-039f-4abe-ab65-c4f9c368b9e8

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